Project description:The radiation bystander effect is an important component of the overall biological response of tissues and organisms to ionizing radiation. Little is known about the contribution of genome level changes in neighboring bystander cells to tissue and organ stress after irradiation. The timing of these changes is critical in the physiological context and these questions can only be answered by studying signaling and global transcriptomics in a chronological way. Here, we present a strategy to identify different biologically important signaling modules that act in concert in the radiation and bystander responses. We used time series gene expression analysis of normal human fibroblast cells measured at 0.5 hour, 1 hour, 2 hours, 4 hours, 6 hours and 24 hours after exposure to radiation coupled with a novel clustering method targeted to short time series, Feature Based Partitioning around medoids Algorithm (FBPA), to look for genes that were potentially co-regulated. This method uses biologically meaningful features of the expression profile and dimension augmentation to address the analysis of sparse data sets such as ours. We applied FBPA and Short Time series Expression Miner (STEM) to the same datasets and present the results of our comparisons using computational metrics as well as biological enrichment. Enrichment showed that gene expression in irradiated cells fell into broad categories of signal transduction, cell cycle/cell death and inflammation/immunity; but only FBPA clustered functions well. In bystander cells, the gene expression response was also broadly categorized into functions associated with cell communication and motility, signal transduction and inflammation; but neither STEM nor FBPA separated biological functions as well as in irradiated samples. Network analysis revealed that p53 and NF-kappaB were central players in gene expression in both irradiated and bystander gene clusters. Analysis of individual clusters also suggested new regulators of gene expression in the radiation and bystander response that may act at the epigenetic level such as histone deacetylases (HDAC1 and HDAC2) and methylases (KDM5B) that can act as strong transcription repressors. Based on these results, we propose a novel time series clustering method, FBPA, as a powerful approach that can be applied to sparse data sets (including genomic profiling data), where the choice of features selected for clustering and stringent statistical outcome analysis can augment our knowledge of the underlying cellular mechanisms in biological processes. There are 72 total samples, 4 corresponding biological replicates of IMR90 cells that were not irradiated (control=C), irradiated (alpha=A) and bystander (B), cells were harvested at 0.5 hour, 1 hour, 2 hours, 4 hours, 6 hours and 24 hours after treatment

Project description:The radiation bystander effect is an important component of the overall biological response of tissues and organisms to ionizing radiation. Little is known about the contribution of genome level changes in neighboring bystander cells to tissue and organ stress after irradiation. The timing of these changes is critical in the physiological context and these questions can only be answered by studying signaling and global transcriptomics in a chronological way. Here, we present a strategy to identify different biologically important signaling modules that act in concert in the radiation and bystander responses. We used time series gene expression analysis of normal human fibroblast cells measured at 0.5 hour, 1 hour, 2 hours, 4 hours, 6 hours and 24 hours after exposure to radiation coupled with a novel clustering method targeted to short time series, Feature Based Partitioning around medoids Algorithm (FBPA), to look for genes that were potentially co-regulated. This method uses biologically meaningful features of the expression profile and dimension augmentation to address the analysis of sparse data sets such as ours. We applied FBPA and Short Time series Expression Miner (STEM) to the same datasets and present the results of our comparisons using computational metrics as well as biological enrichment. Enrichment showed that gene expression in irradiated cells fell into broad categories of signal transduction, cell cycle/cell death and inflammation/immunity; but only FBPA clustered functions well. In bystander cells, the gene expression response was also broadly categorized into functions associated with cell communication and motility, signal transduction and inflammation; but neither STEM nor FBPA separated biological functions as well as in irradiated samples. Network analysis revealed that p53 and NF-kappaB were central players in gene expression in both irradiated and bystander gene clusters. Analysis of individual clusters also suggested new regulators of gene expression in the radiation and bystander response that may act at the epigenetic level such as histone deacetylases (HDAC1 and HDAC2) and methylases (KDM5B) that can act as strong transcription repressors. Based on these results, we propose a novel time series clustering method, FBPA, as a powerful approach that can be applied to sparse data sets (including genomic profiling data), where the choice of features selected for clustering and stringent statistical outcome analysis can augment our knowledge of the underlying cellular mechanisms in biological processes.

Project description:The existence of a radiation bystander effect, in which non-irradiated cells respond to signals from irradiated cells, is now well established. It raises concerns for the interpretation of risks arising from exposure to low doses of ionizing radiation. However, the regulatory mechanisms involved in the bystander response have not been well elucidated. To provide insight into the signaling pathways responding in bystanders, we have measured global gene expression four hours after bystander and direct alpha particle exposure of primary human lung fibroblasts.Although common p53-regulated radiation response genes like CDKN1A were expressed at elevated levels in the directly exposed cultures, they showed little or no change in the bystanders. In contrast, genes regulated by NFkappaB, such as PTGS2 (cyclooxygenase-2), IL8 and BCL2A1, responded nearly identically in bystander and irradiated cells. This trend was substantiated by gene ontology and pathway analyses of the microarray data, which suggest that bystander cells mount a full NFkappaB response, but a muted or partial p53 response. In time-course analyses, quantitative real-time PCR measurements of CDKN1A showed the expected 4-hour peak of expression in irradiated but not bystander cells. In contrast, PTGS2, IL8 and BCL2A1 responded with two waves of expression in both bystander and directly irradiated cells, one peaking at half an hour and the other between four and six hours after irradiation.Two major transcriptional hubs that regulate the direct response to ionizing radiation are also implicated in regulation of the bystander response, but to dramatically different degrees. While activation of the p53 response pathway is minimal in bystander cells, the NFkappaB response is virtually identical in irradiated and bystander cells. This alteration in the balance of signaling is likely to lead to different outcomes in irradiated cells and their bystanders, perhaps leading to greater survival of bystanders and increased risk from any long-term damage they have sustained.

Dataset Information

Identification of gene-based responses in human blood cells exposed to alpha particle radiation.

Publications

Identification of gene-based responses in human blood cells exposed to alpha particle radiation.

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